Mortgage Indebtedness and Household Financial Distress

Georgarakos, Dimitris; Lojschova, Adriana; Ward-Warmedinger, Melanie E.

doi:10.2139/ssrn.1456593

Cited by 43 publications

(54 citation statements)

References 30 publications

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“…These data are those reported and discussed in many other studies on this topic (Duygan-Bump and Grant, 2009;Georgarakos et al, 2009;European Commission, 2008). Nevertheless, they derive from very different situations.…”

Section: Data and Definitionssupporting

confidence: 87%

“…A specific contribution of this paper to the analysis of the probability of delinquency is that we focus on the percentage of households with mortgages who are late in making payments, while other studies tend to emphasize the frequency of delinquent households in relation to the total population (European Commission, 2008;Duygan-Bump and Grant, 2009;Georgarakos et al, 2009). An interesting result of our analysis is that in Italy roughly 5% of households with mortgages were delinquent in 2007, the highest value, together with Spain, among the seven European countries under analysis; similar results are found for the other waves, i.e., 2005 and 2006.…”

Section: Introductionmentioning

confidence: 99%

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The rise of risk-based pricing of mortgage interest rates in Italy

Magri

Pico

2011

Journal of Banking & Finance

269

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Section: Data and Definitionssupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

The rise of risk-based pricing of mortgage interest rates in Italy

Magri

Pico

2011

Journal of Banking & Finance

269

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“…In most cases, the estimates remain within the 95 per cent confidence interval from the OLS regressions (not shown, but available in a web appendix). 20 Moreover, we find that most of the variation in the effect of household characteristics across the debt distri- 20 However, there are some exceptions. Notably, in a number of countries the age effects vary along the debt distribution, and on occasion the estimates move outside the OLS 95 per cent confidence interval bounds.…”

Section: The Amount Of Debt Heldmentioning

confidence: 80%

“…Regarding the amount of secured debt held, the impacts of time to repossess vary substantially across cohort/age groups.Conditional on obtaining a secured loan, a 15 months longer repossession period decreases by 12 per cent the amount of debt held by the set of households aged [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] and with characteristics other than age held at those of the reference group. 41 .…”

Section: Economic Magnitude Of the Responsesmentioning

confidence: 99%

The Distribution of Debt Across Euro Area Countries: The Role of Individual Characteristics, Institutions and Credit Conditions

et al. 2013

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The HFCN collects household-level data on households' finances and consumption in the euro area through a harmonised survey. The HFCN aims at studying in depth the micro-level structural information on euro area households' assets and liabilities. The objectives of the network are: Terms of use: Documents in EconStor may1) understanding economic behaviour of individual households, developments in aggregate variables and the interactions between the two;2) evaluating the impact of shocks, policies and institutional changes on household portfolios and other variables;3) understanding the implications of heterogeneity for aggregate variables; 4) estimating choices of different households and their reaction to economic shocks; 5) building and calibrating realistic economic models incorporating heterogeneous agents; 6) gaining insights into issues such as monetary policy transmission and financial stability.The refereeing process of this paper has been co-ordinated by a team composed of Gabriel Fagan (ECB), Pirmin Fessler (Oesterreichische Nationalbank), Michalis Haliassos (Goethe University Frankfurt) , Tullio Jappelli (University of Naples Federico II), Sébastien PérezDuarte (ECB), Jiri Slacalek (ECB), Federica Teppa (De Nederlandsche Bank), Peter Tufano (Oxford University) and Philip Vermeulen (ECB).The paper is released in order to make the results of HFCN research generally available, in preliminary form, to encourage comments and suggestions prior to final publication. The views expressed in the paper are the author's own and do not necessarily reflect those of the ESCB. AcknowledgementsThe views expressed in this paper are those of the authors and do not necessarily reflect those of the respective National Central Banks or the European Central Bank. We would like to thank Asa Johansson for providing data on pre-and after-tax mortgage interest rates and Richard Blundell and an anonymous referee for helpful comments. All remaining errors are our own. Olympia Bover (corresponding author)Banco de España; e-mail: bover@bde.es Banco de EspañaOlympia Bover, Jose Maria Casado and Ernesto Villanueva Banco de Portugal Sonia Costa National Bank of Belgium Philip Du Caju Central Bank of Ireland Yvonne McCarthy CEPS / INSTEAD Research Institute Eva Sierminska Bank of Greece and Deutsche Bundesbank Panagiota Tzamourani National Bank of Slovakia Tibor ZavadilAbstract The aim of this paper is twofold. First, we present an up-to-date assessment of the differences across...

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“…According to such authors, the number of household members is shown to positively affect LTV. Moreover, according to Georgarakos, Lojschova and Ward-Warmedinger [20], households may have not fully taken into account the costs associated with children before deciding to borrow and thus children show up as an additional source of distress when servicing mortgage debt.…”

Section: Profiles Building and Interpretationmentioning

confidence: 99%

Indebted Households Profiling: A Knowledge Discovery from Database Approach

2015

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Abstract:A major challenge in consumer credit risk portfolio management is to classify households according to their risk profile. In order to build such risk profiles it is necessary to employ an approach that analyses data systematically in order to detect important relationships, interactions, dependencies and associations amongst the available continuous and categorical variables altogether and accurately generate profiles of most interesting household segments according to their credit risk. The objective of this work is to employ a knowledge discovery from database process to identify groups of indebted households and describe their profiles using a database collected by the Consumer Credit Counselling Service (CCCS) in the UK. Employing a framework that allows the usage of both categorical and continuous data altogether to find hidden structures in unlabelled data it was established the ideal number of clusters and such clusters were described in order to identify the households who exhibit a high propensity of excessive debt levels.

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Mortgage Indebtedness and Household Financial Distress

Abstract: 4Non-technical summary 5

Cited by 43 publications

References 30 publications

The rise of risk-based pricing of mortgage interest rates in Italy

The rise of risk-based pricing of mortgage interest rates in Italy

The Distribution of Debt Across Euro Area Countries: The Role of Individual Characteristics, Institutions and Credit Conditions

Indebted Households Profiling: A Knowledge Discovery from Database Approach

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